Systems and methods for processing electronic images for computational detection methods

ABSTRACT

Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. application Ser. No.17/480,826, filed on Sep. 21, 2021, which is a continuation of U.S.application Ser. No. 17/159,849, filed on Jan. 27, 2021, which claimspriority to U.S. Provisional Application No. 62/966,716 filed Jan. 28,2020, the entire disclosures of which is are hereby incorporated hereinby reference in their entireties.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure pertain generally tocreating a prediction model to predict labels for prepared tissuespecimens by processing electronic images. More specifically, particularembodiments of the present disclosure relate to systems and methods forpredicting, identifying or detecting diagnosis information aboutprepared tissue specimens. The present disclosure further providessystems and methods for creating a prediction model that predicts labelsfrom unseen slides.

BACKGROUND

The performance of machine learning and deep learning models forhistopathology may be limited by the volume and quality of annotatedexamples used to train these models. Large-scale experiments onsupervised image classification problems have shown that modelperformance continues to improve, up through an order of 50 milliontraining examples. Manually annotating this volume of data may beprohibitively expensive both in time and cost, and it can be a severelimitation in ensuring systems perform at a clinically relevant leveland generalize across institutions.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure. The background description provided herein is for thepurpose of generally presenting the context of the disclosure. Unlessotherwise indicated herein, the materials described in this section arenot prior art to the claims in this application and are not admitted tobe prior art, or suggestions of the prior art, by inclusion in thissection.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for developing weakly supervised multi-label andmulti-task learning for computational biomarker detection in digitalpathology.

A computer-implemented method for processing an electronic imagecorresponding to a specimen includes: receiving one or more digitalimages associated with a tissue specimen, receiving one or moreelectronic slide images associated with a tissue specimen, the tissuespecimen being associated with a patient and/or medical case;partitioning a first slide image of the one or more electronic slideimages into a plurality of tiles; detecting a plurality of tissueregions of the first slide image and/or plurality of tiles to generate atissue mask; determining whether any of the plurality of tilescorresponds to non-tissue; removing any of the plurality of tiles thatare determined to be non-tissue; determining a prediction, using amachine learning prediction model, for at least one label for the one ormore electronic slide images, the machine learning prediction modelhaving been generated by processing a plurality of training images; andoutputting the prediction of the trained machine learning predictionmodel.

A system for processing an electronic image corresponding to a specimenincludes a memory storing instructions; and at least one processorexecuting the instructions to perform a process including receiving oneor more digital images associated with a tissue specimen, receiving oneor more electronic slide images associated with a tissue specimen, thetissue specimen being associated with a patient and/or medical case;partitioning a first slide image of the one or more electronic slideimages into a plurality of tiles; detecting a plurality of tissueregions of the first slide image and/or plurality of tiles to generate atissue mask; determining whether any of the plurality of tilescorresponds to non-tissue; removing any of the plurality of tiles thatare determined to be non-tissue; determining a prediction, using amachine learning prediction model, for at least one label for the one ormore electronic slide images, the machine learning prediction modelhaving been generated by processing a plurality of training images; andoutputting the prediction of the trained machine learning predictionmodel.

A non-transitory computer-readable medium storing instructions that,when executed by a processor, cause the processor to perform a methodfor processing an electronic image corresponding to a specimen includes:receiving one or more digital images associated with a tissue specimen,receiving one or more electronic slide images associated with a tissuespecimen, the tissue specimen being associated with a patient and/ormedical case; partitioning a first slide image of the one or moreelectronic slide images into a plurality of tiles; detecting a pluralityof tissue regions of the first slide image and/or plurality of tiles togenerate a tissue mask; determining whether any of the plurality oftiles corresponds to non-tissue; removing any of the plurality of tilesthat are determined to be non-tissue; determining a prediction, using amachine learning prediction model, for at least one label for the one ormore electronic slide images, the machine learning prediction modelhaving been generated by processing a plurality of training images; andoutputting the prediction of the trained machine learning predictionmodel.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1A illustrates an exemplary block diagram of a system and networkfor creating a prediction model, according to an exemplary embodiment ofthe present disclosure.

FIG. 1B illustrates an exemplary block diagram of a prediction modelplatform, according to an exemplary embodiment of the presentdisclosure.

FIG. 1C illustrates an exemplary block diagram of a slide analysis tool,according to an exemplary embodiment of the present disclosure.

FIG. 2A is a flowchart illustrating an exemplary method for using aprediction model created by a trained machine learning system, accordingto one or more exemplary embodiments of the present disclosure.

FIG. 2B is a flowchart illustrating an exemplary method for training aweakly supervised tile-level learning module in a trained machinelearning system, according to one or more exemplary embodiments of thepresent disclosure.

FIG. 2C is a flowchart illustrating an exemplary method for training aweakly supervised aggregation module in a trained machine learningsystem, according to one or more exemplary embodiments of the presentdisclosure.

FIG. 3 is a flowchart illustrating an exemplary method for training andusing a machine learning system to simultaneously detect and gradeprostate cancer, according to one or more exemplary embodiments of thepresent disclosure.

FIG. 4 is a flowchart illustrating an exemplary method for training andusing a machine learning system for tumor quantification in prostateneedle biopsies, according to one or more exemplary embodiments of thepresent disclosure.

FIG. 5 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting a cancer subtype,according to one or more exemplary embodiments of the presentdisclosure.

FIG. 6 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting a surgical margin,according to one or more exemplary embodiments of the present disclosure

FIG. 7 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting a bladder cancerbiomarker, according to one or more exemplary embodiments of the presentdisclosure.

FIG. 8 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting a pan-cancer diagnosis,according to one or more exemplary embodiments of the presentdisclosure.

FIG. 9 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting an organ toxicity,according to one or more exemplary embodiments of the presentdisclosure.

FIG. 10 illustrates an exemplary connected components algorithm,according to an embodiment of the disclosure.

FIG. 11 depicts an exemplary system that may execute techniquespresented herein.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.

The systems, devices, and methods disclosed herein are described indetail by way of examples and with reference to the figures. Theexamples discussed herein are examples only and are provided to assistin the explanation of the apparatuses, devices, systems, and methodsdescribed herein. None of the features or components shown in thedrawings or discussed below should be taken as mandatory for anyspecific implementation of any of these devices, systems, or methodsunless specifically designated as mandatory.

Also, for any methods described, regardless of whether the method isdescribed in conjunction with a flow diagram, it should be understoodthat unless otherwise specified or required by context, any explicit orimplicit ordering of steps performed in the execution of a method doesnot imply that these steps must be performed in the order presented butmay instead by performed in a different order or in parallel.

As used herein, the term “exemplary” is used in the sense of “example,”rather than “ideal.” Moreover, the terms “a” and “an” herein do notdenote a limitation of quantity, but rather denote the presence of oneor more of the referenced items.

Pathology refers to the study of diseases, as well as the causes andeffects of disease. More specifically, pathology refers to performingtests and analysis that are used to diagnose diseases. For example,tissue samples may be placed onto slides to be viewed under a microscopeby a pathologist (e.g., a physician that is an expert at analyzingtissue samples to determine whether any abnormalities exist). That is,pathology specimens may be cut into multiple sections, stained, andprepared as slides for a pathologist to examine and render a diagnosis.When uncertain of a diagnostic finding on a slide, a pathologist mayorder additional cut levels, stains, or other tests to gather moreinformation from the tissue. Technician(s) may then create new slide(s)that may contain the additional information for the pathologist to usein making a diagnosis. This process of creating additional slides may betime-consuming, not only because it may involve retrieving the block oftissue, cutting it to make a new a slide, and then staining the slide,but also because it may be batched for multiple orders. This maysignificantly delay the final diagnosis that the pathologist renders. Inaddition, even after the delay, there may still be no assurance that thenew slide(s) will have information sufficient to render a diagnosis.

Pathologists may evaluate cancer and other disease pathology slides inisolation. The present disclosure presents a consolidated workflow forimproving diagnosis of cancer and other diseases. The workflow mayintegrate, for example, slide evaluation, tasks, image analysis andcancer detection artificial intelligence (AI), annotations,consultations, and recommendations in one workstation. In particular,the present disclosure describes various exemplary user interfacesavailable in the workflow, as well as AI tools that may be integratedinto the workflow to expedite and improve a pathologist's work.

For example, computers may be used to analyze an image of a tissuesample to quickly identify whether additional information may be neededabout a particular tissue sample, and/or to highlight to a pathologistan area in which he or she should look more closely. Thus, the processof obtaining additional stained slides and tests may be doneautomatically before being reviewed by a pathologist. When paired withautomatic slide segmenting and staining machines, this may provide afully automated slide preparation pipeline. This automation has, atleast, the benefits of (1) minimizing an amount of time wasted by apathologist determining a slide to be insufficient to make a diagnosis,(2) minimizing the (average total) time from specimen acquisition todiagnosis by avoiding the additional time between when additional testsare ordered and when they are produced, (3) reducing the amount of timeper recut and the amount of material wasted by allowing recuts to bedone while tissue blocks (e.g., pathology specimens) are in a cuttingdesk, (4) reducing the amount of tissue material wasted/discarded duringslide preparation, (5) reducing the cost of slide preparation bypartially or fully automating the procedure, (6) allowing automaticcustomized cutting and staining of slides that would result in morerepresentative/informative slides from samples, (7) allowing highervolumes of slides to be generated per tissue block, contributing to moreinformed/precise diagnoses by reducing the overhead of requestingadditional testing for a pathologist, and/or (8) identifying orverifying correct properties (e.g., pertaining to a specimen type) of adigital pathology image, etc.

The process of using computers to assist pathologists is known ascomputational pathology. Computing methods used for computationalpathology may include, but are not limited to, statistical analysis,autonomous or machine learning, and AI. AI may include, but is notlimited to, deep learning, neural networks, classifications, clustering,and regression algorithms. By using computational pathology, lives maybe saved by helping pathologists improve their diagnostic accuracy,reliability, efficiency, and accessibility. For example, computationalpathology may be used to assist with detecting slides suspicious forcancer, thereby allowing pathologists to check and confirm their initialassessments before rendering a final diagnosis.

As described above, computational pathology processes and devices of thepresent disclosure may provide an integrated platform allowing a fullyautomated process including data ingestion, processing and viewing ofdigital pathology images via a web-browser or other user interface,while integrating with a laboratory information system (LIS). Further,clinical information may be aggregated using cloud-based data analysisof patient data. The data may come from hospitals, clinics, fieldresearchers, etc., and may be analyzed by machine learning, computervision, natural language processing, and/or statistical algorithms to doreal-time monitoring and forecasting of health patterns at multiplegeographic specificity levels.

Histopathology refers to the study of a specimen that has been placedonto a slide. For example, a digital pathology image may be comprised ofa digitized image of a microscope slide containing the specimen (e.g., asmear). One method a pathologist may use to analyze an image on a slideis to identify nuclei and classify whether a nucleus is normal (e.g.,benign) or abnormal (e.g., malignant). To assist pathologists inidentifying and classifying nuclei, histological stains may be used tomake cells visible. Many dye-based staining systems have been developed,including periodic acid-Schiff reaction, Masson's trichrome, nissl andmethylene blue, and Haemotoxylin and Eosin (H&E). For medical diagnosis,H&E is a widely used dye based method, with hematoxylin staining cellnuclei blue, eosin staining cytoplasm and extracellular matrix pink, andother tissue regions taking on variations of these colors. In manycases, however, H&E-stained histologic preparations do not providesufficient information for a pathologist to visually identify biomarkersthat can aid diagnosis or guide treatment. In this situation, techniquessuch as immunohistochemistry (IHC), immunofluorescence, in situhybridization (ISH), or fluorescence in situ hybridization (FISH), maybe used. IHC and immunofluorescence involve, for example, usingantibodies that bind to specific antigens in tissues enabling the visualdetection of cells expressing specific proteins of interest, which canreveal biomarkers that are not reliably identifiable to trainedpathologists based on the analysis of H&E stained slides. ISH and FISHmay be employed to assess the number of copies of genes or the abundanceof specific RNA molecules, depending on the type of probes employed(e.g. DNA probes for gene copy number and RNA probes for the assessmentof RNA expression). If these methods also fail to provide sufficientinformation to detect some biomarkers, genetic testing of the tissue maybe used to confirm if a biomarker is present (e.g., overexpression of aspecific protein or gene product in a tumor, amplification of a givengene in a cancer).

A digitized image may be prepared to show a stained microscope slide,which may allow a pathologist to manually view the image on a slide andestimate a number of stained abnormal cells in the image. However, thisprocess may be time consuming and may lead to errors in identifyingabnormalities because some abnormalities are difficult to detect.Computational processes and devices may be used to assist pathologistsin detecting abnormalities that may otherwise be difficult to detect.For example, AI may be used to predict biomarkers (such as theoverexpression of a protein and/or gene product, amplification, ormutations of specific genes) from salient regions within digital imagesof tissues stained using H&E and other dye-based methods. The images ofthe tissues could be whole slide images (WSI), images of tissue coreswithin microarrays or selected areas of interest within a tissuesection. Using staining methods like H&E, these biomarkers may bedifficult for humans to visually detect or quantify without the aid ofadditional testing. Using AI to infer these biomarkers from digitalimages of tissues has the potential to improve patient care, while alsobeing faster and less expensive.

The detected biomarkers or the image alone could then be used torecommend specific cancer drugs or drug combination therapies to be usedto treat a patient, and the AI could identify which drugs or drugcombinations are unlikely to be successful by correlating the detectedbiomarkers with a database of treatment options. This can be used tofacilitate the automatic recommendation of immunotherapy drugs to targeta patient's specific cancer. Further, this could be used for enablingpersonalized cancer treatment for specific subsets of patients and/orrarer cancer types.

As described above, computational pathology processes and devices of thepresent disclosure may provide an integrated platform allowing a fullyautomated process including data ingestion, processing and viewing ofdigital pathology images via a web-browser or other user interface,while integrating with a laboratory information system (LIS). Further,clinical information may be aggregated using cloud-based data analysisof patient data. The data may come from hospitals, clinics, fieldresearchers, etc., and may be analyzed by machine learning, computervision, natural language processing, and/or statistical algorithms to doreal-time monitoring and forecasting of health patterns at multiplegeographic specificity levels.

The digital pathology images described above may be stored with tagsand/or labels pertaining to the properties of the specimen or thedigital pathology image and such tags/labels may be incomplete.Accordingly, systems and methods disclosed herein predict at least onelabel from a collection of digital images.

The performance of machine learning and deep learning models forhistopathology may be limited by the volume and quality of annotatedexamples used to train these models. Large-scale experiments onsupervised image classification problems have shown that modelperformance continues to improve, up through an order of 50 milliontraining examples. Most clinically relevant tasks in pathology entailmuch more than classification, however. When a pathologist renders adiagnosis, the diagnosis may take the form of a report that containsmany heterogeneous interrelated fields and pertains to an entire slideor set of slides. In oncology, these fields can include the presence ofcancer, cancer grades, tumor quantification, cancer grade group,presence of various features important for staging of the cancer, etc.In pre-clinical drug research animal studies, these fields could includethe presence of toxicity, the severity of toxicity, and the kind oftoxicity. Procuring the necessary annotations to train most superviseddeep learning models may involve a pathologist labeling individualpixels, tiles (e.g., one or more relatively small rectangular regions ina slide image), or regions of interest (e.g., polygons) from the slideimage with an appropriate annotation. For each field in the report, adifferent set of training annotations may be used. Furthermore, atypical digital pathology slide can contain on the order of 10gigapixels, or more than 100,000 tiles. Manually annotating this volumeof data may be prohibitively expensive both in time and cost, and it canbe a severe limitation in ensuring systems perform at a clinicallyrelevant level and generalize across institutions. Accordingly, a desireexists to generate training data that can be used for histopathology.

The embodiments of the present disclosure may overcome the abovelimitations. In particular, embodiments disclosed herein may use weaksupervision, in which a deep learning model may be trained directly froma pathologist's diagnosis, rather than with additional labeling of eachpixel or tile in a digital image. A machine learning or deep learningmodel may comprise a machine learning algorithm, in some embodiments.One technique may determine binary cancer detection, however techniquesdiscussed herein further disclose, for example, how a deep learningsystem may be trained in a weakly supervised multi-label and multi-tasksetting to perform grading, subtyping, inferring multiple diseaseattributes simultaneously, and more. This enables systems to be traineddirectly from diagnostic reports or test results without the need forextensive annotations, reducing the number of required training labelsby five orders of magnitude or more.

The disclosed systems and methods may automatically predict the specimenor image properties, without relying on the stored tags or labels.Further, systems and methods are disclosed for quickly and correctlyidentifying and/or verifying a specimen type of a digital pathologyimage, or any information related to a digital pathology image, withoutnecessarily accessing an LIS or analogous information database. Oneembodiment of the present disclosure may include a system trained toidentify various properties of a digital pathology image, based ondatasets of prior digital pathology images. The trained system mayprovide a classification for a specimen shown in a digital pathologyimage. The classification may help to provide treatment or diagnosisprediction(s) for a patient associated with the specimen.

This disclosure includes one or more embodiments of a slide analysistool. The input to the tool may include a digital pathology image andany relevant additional inputs. Outputs of the tool may include globaland/or local information about the specimen. A specimen may include abiopsy or surgical resection specimen.

FIG. 1A illustrates a block diagram of a system and network fordetermining specimen property or image property information pertainingto digital pathology image(s), using machine learning, according to anexemplary embodiment of the present disclosure.

Specifically, FIG. 1A illustrates an electronic network 120 that may beconnected to servers at hospitals, laboratories, and/or doctors'offices, etc. For example, physician servers 121, hospital servers 122,clinical trial servers 123, research lab servers 124, and/or laboratoryinformation systems 125, etc., may each be connected to an electronicnetwork 120, such as the Internet, through one or more computers,servers, and/or handheld mobile devices. According to an exemplaryembodiment of the present application, the electronic network 120 mayalso be connected to server systems 110, which may include processingdevices that are configured to implement a disease detection platform100, which includes a slide analysis tool 101 for determining specimenproperty or image property information pertaining to digital pathologyimage(s), and using machine learning to classify a specimen, accordingto an exemplary embodiment of the present disclosure.

The physician servers 121, hospital servers 122, clinical trial servers123, research lab servers 124, and/or laboratory information systems 125may create or otherwise obtain images of one or more patients' cytologyspecimen(s), histopathology specimen(s), slide(s) of the cytologyspecimen(s), digitized images of the slide(s) of the histopathologyspecimen(s), or any combination thereof. The physician servers 121,hospital servers 122, clinical trial servers 123, research lab servers124, and/or laboratory information systems 125 may also obtain anycombination of patient-specific information, such as age, medicalhistory, cancer treatment history, family history, past biopsy orcytology information, etc. The physician servers 121, hospital servers122, clinical trial servers 123, research lab servers 124, and/orlaboratory information systems 125 may transmit digitized slide imagesand/or patient-specific information to server systems 110 over theelectronic network 120. Server systems 110 may include one or morestorage devices 109 for storing images and data received from at leastone of the physician servers 121, hospital servers 122, clinical trialservers 123, research lab servers 124, and/or laboratory informationsystems 125. Server systems 110 may also include processing devices forprocessing images and data stored in the one or more storage devices109. Server systems 110 may further include one or more machine learningtool(s) or capabilities. For example, the processing devices may includea machine learning tool for a disease detection platform 100, accordingto one embodiment. Alternatively or in addition, the present disclosure(or portions of the system and methods of the present disclosure) may beperformed on a local processing device (e.g., a laptop).

The physician servers 121, hospital servers 122, clinical trial servers123, research lab servers 124, and/or laboratory information systems 125refer to systems used by pathologists for reviewing the images of theslides. In hospital settings, tissue type information may be stored in alaboratory information systems 125. However, the correct tissueclassification information is not always paired with the image content.Additionally, even if an LIS is used to access the specimen type for adigital pathology image, this label may be incorrect due to the factthat many components of an LIS may be manually inputted, leaving a largemargin for error. According to an exemplary embodiment of the presentdisclosure, a specimen type may be identified without needing to accessthe laboratory information systems 125, or may be identified to possiblycorrect laboratory information systems 125. For example, a third partymay be given anonymized access to the image content without thecorresponding specimen type label stored in the LIS. Additionally,access to LIS content may be limited due to its sensitive content.

FIG. 1B illustrates an exemplary block diagram of a disease detectionplatform 100 for determining specimen property or image propertyinformation pertaining to digital pathology image(s), using machinelearning. For example, the disease detection platform 100 may include aslide analysis tool 101, a data ingestion tool 102, a slide intake tool103, a slide scanner 104, a slide manager 105, a storage 106, and aviewing application tool 108.

The slide analysis tool 101, as described below, refers to a process andsystem for processing digital images associated with a tissue specimen,and using machine learning to analyze a slide, according to an exemplaryembodiment.

The data ingestion tool 102 refers to a process and system forfacilitating a transfer of the digital pathology images to the varioustools, modules, components, and devices that are used for classifyingand processing the digital pathology images, according to an exemplaryembodiment.

The slide intake tool 103 refers to a process and system for scanningpathology images and converting them into a digital form, according toan exemplary embodiment. The slides may be scanned with slide scanner104, and the slide manager 105 may process the images on the slides intodigitized pathology images and store the digitized images in storage106.

The viewing application tool 108 refers to a process and system forproviding a user (e.g., a pathologist) with specimen property or imageproperty information pertaining to digital pathology image(s), accordingto an exemplary embodiment. The information may be provided throughvarious output interfaces (e.g., a screen, a monitor, a storage device,and/or a web browser, etc.).

The slide analysis tool 101, and each of its components, may transmitand/or receive digitized slide images and/or patient information toserver systems 110, physician servers 121, hospital servers 122,clinical trial servers 123, research lab servers 124, and/or laboratoryinformation systems 125 over an electronic network 120. Further, serversystems 110 may include one or more storage devices 109 for storingimages and data received from at least one of the slide analysis tool101, the data ingestion tool 102, the slide intake tool 103, the slidescanner 104, the slide manager 105, and viewing application tool 108.Server systems 110 may also include processing devices for processingimages and data stored in the storage devices. Server systems 110 mayfurther include one or more machine learning tool(s) or capabilities,e.g., due to the processing devices. Alternatively or in addition, thepresent disclosure (or portions of the system and methods of the presentdisclosure) may be performed on a local processing device (e.g., alaptop).

Any of the above devices, tools and modules may be located on a devicethat may be connected to an electronic network 120, such as the Internetor a cloud service provider, through one or more computers, servers,and/or handheld mobile devices.

FIG. 1C illustrates an exemplary block diagram of a slide analysis tool101, according to an exemplary embodiment of the present disclosure. Theslide analysis tool 101 may include a training image platform 131 and/ora target image platform 135.

The training image platform 131, according to one embodiment, may createor receive training images that are used to train a machine learningsystem to effectively analyze and classify digital pathology images. Forexample, the training images may be received from any one or anycombination of the server systems 110, physician servers 121, hospitalservers 122, clinical trial servers 123, research lab servers 124,and/or laboratory information systems 125. Images used for training maycome from real sources (e.g., humans, animals, etc.) or may come fromsynthetic sources (e.g., graphics rendering engines, 3D models, etc.).Examples of digital pathology images may include (a) digitized slidesstained with a variety of stains, such as (but not limited to) H&E,Hemotoxylin alone, IHC, molecular pathology, etc.; and/or (b) digitizedtissue samples from a 3D imaging device, such as microCT.

The training image intake module 132 may create or receive a datasetcomprising one or more training images corresponding to either or bothof images of a human tissue and images that are graphically rendered.For example, the training images may be received from any one or anycombination of the server systems 110, physician servers 121, hospitalservers 122, clinical trial servers 123, research lab servers 124,and/or laboratory information systems 125. This dataset may be kept on adigital storage device. The quality score determiner module 133 mayidentify quality control (QC) issues (e.g., imperfections) for thetraining images at a global or local level that may greatly affect theusability of a digital pathology image. For example, the quality scoredeterminer module may use information about an entire image, e.g., thespecimen type, the overall quality of the cut of the specimen, theoverall quality of the glass pathology slide itself, or tissuemorphology characteristics, and determine an overall quality score forthe image. The treatment identification module 134 may analyze images oftissues and determine which digital pathology images have treatmenteffects (e.g., post-treatment) and which images do not have treatmenteffects (e.g., pretreatment). It is useful to identify whether a digitalpathology image has treatment effects because prior treatment effects intissue may affect the morphology of the tissue itself. Most LIS do notexplicitly keep track of this characteristic, and thus classifyingspecimen types with prior treatment effects can be desired.

According to one embodiment, the target image platform 135 may include atarget image intake module 136, a specimen detection module 137, and anoutput interface 138. The target image platform 135 may receive a targetimage and apply the machine learning model to the received target imageto determine a characteristic of a target specimen. For example, thetarget image may be received from any one or any combination of theserver systems 110, physician servers 121, hospital servers 122,clinical trial servers 123, research lab servers 124, and/or laboratoryinformation systems 125. The target image intake module 136 may receivea target image corresponding to a target specimen. The specimendetection module 137 may apply the machine learning model to the targetimage to determine a characteristic of the target specimen. For example,the specimen detection module 137 may detect a specimen type of thetarget specimen. The specimen detection module 137 may also apply themachine learning model to the target image to determine a quality scorefor the target image. Further, the specimen detection module 137 mayapply the machine learning model to the target specimen to determinewhether the target specimen is pretreatment or post-treatment.

The output interface 138 may be used to output information about thetarget image and the target specimen (e.g., to a screen, monitor,storage device, web browser, etc.).

FIG. 2A is a flowchart illustrating an exemplary method for using aprediction model created by a trained machine learning system, accordingto one or more exemplary embodiments of the present disclosure. Forexample, an exemplary method 200 (steps 202-210) may be performed byslide analysis tool 101 automatically or in response to a request from auser.

According to one embodiment, the exemplary method 200 for using aprediction model may include one or more of the following steps. In step202, the method may include receiving one or more digital imagesassociated with a tissue specimen, wherein the one or more digital imagecomprises a plurality of slide images. The digital storage device maycomprise a hard drive, a network drive, a cloud storage, a random accessmemory (RAM), or any other suitable storage device.

In step 204, the method may include partitioning one of the plurality ofslide images into a collection of tiles for the plurality of slideimages.

In step 206, the method may include detecting a plurality of tissueregions from a background of the one of plurality of slide images tocreate a tissue mask and removing at least one tile of the collection oftiles that is detected to be non-tissue. The tile that is non-tissue maycomprise a background of the slide image. This may be accomplished in avariety of ways, including: thresholding based methods based on color,color intensity, texture features or Otsu's method, followed by runninga connected components algorithm; segmentation algorithms, such ask-means, graph cuts, mask region convolutional neural network (MaskR-CNN); or any other suitable methods.

In step 208, the method may include determining a prediction, using amachine learning system, for a label for the plurality of slide imagescorresponding to a patient or medical case, the machine learning systemhaving been generated by processing a plurality of training examples tocreate a prediction model. The training examples may comprise a set ofone or more digital slide images and a plurality of target labels.

In step 210, the method may include outputting the prediction model ofthe training machine learning system that predicts at least one labelfrom at least one slide that was not used for training the machinelearning system and outputting the prediction to an electronic storagedevice.

FIG. 2B is a flowchart illustrating an exemplary method for training aweakly supervised tile-level learning module in a trained machinelearning system, according to one or more exemplary embodiments of thepresent disclosure. The weakly supervised learning module may train amodel to make tile-level predictions using slide-level training labels.For example, an exemplary method 220 (steps 222-230) may be performed byslide analysis tool 101 automatically or in response to a request from auser.

According to one embodiment, the exemplary method 220 for using aprediction model may include one or more of the following steps. In step222, the method may include receiving a collection of digital imagesassociated with a training tissue specimen into a digital storagedevice, wherein the collection of digital images comprise a plurality oftraining slide images. The digital storage device may comprise a harddrive, a network drive, a cloud storage, a random access memory (RAM),or any other suitable storage device.

In step 224, the method may include receiving a plurality of synopticannotations comprising one or more labels for each of the plurality oftraining slide images. The labels may be binary, multi-level binary,categorical, ordinal or real valued.

In step 226, the method may include partitioning one of the plurality oftraining slide images into a collection of training tiles for theplurality of training slide images.

In step 228, the method may include detecting at least one tissue regionfrom the background of the plurality of training slide images to createa training tissue mask, and removing at least one training tile of thecollection of training tiles that is detected to be non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 230, the method may include training a prediction model underweak supervision to infer at least one multi-label tile-level predictionusing at least one synoptic label. There may be four general approachesfor training a model under a weak-supervision setting, but any suitableapproach to training the model may be used.

1. Multiple Instance Learning (MIL) may be used to train a tile-levelprediction model for binary or categorical labels by learning toidentify tiles that contain a target label of the slide. Thisidentification may be accomplished by finding salient tiles (e.g.,maximal scoring tiles based on received synoptic annotations or labelsat each training iteration), and using these tiles to update aclassifier using the received synoptic training label(s) associated witheach salient tile. For example, the classifier may be trained toidentify cancer based on a collection of overlapping tiles. As salienttiles are determined, the synoptic labels may be used to update thetile-level labels. This tile-level label and classifier may thendetermine or provide a label for a group of tiles. MIL may also be usedto train a machine learning model to extract diagnostic features forother downstream tasks such as cancer grading, cancer subtyping,biomarker detection, etc.2. Multiple Instance Multiple Label Learning (MIMLL) may be a tile-levelprediction model comprising a generalization of MIL that treats eachslide as a set of tiles that may be associated with multiple labels,rather than only a single binary label as in MIL. These slide labels maycome from a pathologist's diagnostic report, genetic testing,immunological testing, or other measurements/assays. The MIMLL model maybe trained to select tiles that correspond to each of the synoptictraining labels belonging to the set of one or more slides. The presentembodiment may involve the MIMLL training a neural network (e.g., aConvolutional Neural Network (CNN), capsule network, etc.) by iteratingthe following steps:

-   -   a. For each label of the labels to be predicted, select the most        relevant set of tiles using a scoring function. The scoring        function may be formulated to rank multiple tiles        simultaneously. For example, with multiple binary labels, a CNN        may be run on each tile that attempts to predict each of the        multiple binary labels from every tile in a set of slides, and        the tiles with the outputs closest to 1 for one or more of the        labels may be selected.    -   b. Use the selected tiles to update the weights of the CNN model        with respect to their associated label assignments. Each label        may have its own output layer in the model.        Similar to the MIL model, the MIMLL model may also be used to        extract diagnostic features for other downstream tasks.        3. Self-supervised learning may use a small amount of tile-level        training data to create an initial tile-based classifier using        supervised learning. This initial classifier may be used to        bootstrap a full training process by alternating the following:    -   a. Reassign tile labels in the training set using predictions        from the current tile-based model.    -   b. Update the model for each tile with respect to the latest        label assignments.        4. Unsupervised clustering may learn to group similar instances        together without the use of target labels. Slide tiles may be        treated as instances, and the number of groupings may either be        pre-specified or learned automatically by the algorithm. Such        clustering algorithms may include, but are not limited to the        following methods:    -   a. Expectation maximization (EM)    -   b. Majorization maximization (MM)    -   c. K-nearest neighbor (KNN)    -   d. Hierarchical clustering    -   e. Agglomerative clustering        The resulting model may be used to extract diagnostic features        to be used by the slide-level prediction module.

FIG. 2C is a flowchart illustrating an exemplary method for training aweakly supervised aggregation module in a trained machine learningsystem, according to one or more exemplary embodiments of the presentdisclosure. For example, an exemplary method 240 (steps 242-244) may beperformed by slide analysis tool 101 automatically or in response to arequest from a user.

According to one embodiment, the exemplary method 240 for training theweakly supervised aggregation module may include one or more of thefollowing steps. In step 242, the method may include receiving aplurality of predictions or a plurality of vectors of at least onefeature from a weakly-supervised tile-level learning module for thecollection of training tiles.

In step 244, the method may include training a machine learning model totake, as an input, the plurality of predictions or the plurality ofvectors of the at least one feature from the weakly-supervisedtile-level learning module for the collection of tiles. This aggregationmodule may train a multi-task slide-level aggregation model to taketile-level inputs and produce a final prediction for the tiles inputinto a system and/or slide images input into a system. A general form ofthe model may be comprised of multiple outputs (e.g., multi-tasklearning), and each label may be binary, categorical, ordinal or realvalued. The tile-level inputs may include image features of any type,including but not limited to:

-   -   a. Outputs (e.g., feature vectors or embeddings) from the weakly        supervised model    -   b. CNN features    -   c. Scale-Invariant Feature Transform (SIFT)    -   d. Speeded-Up Robust Features (SURF)    -   e. Rotation Invariant Feature Transform (RIFT)    -   f. Oriented FAST and Rotated BRIEF (ORB)        The multi-task slide-level aggregation model of the aggregation        module may take many forms, including but not limited to:    -   a. Fully connected neural network trained with multiple output        task groups    -   b. CNN    -   c. Fully-convolutional neural networks    -   d. Recurrent neural network (RNN), including gated recurrent        unit (GRU) and long-short term memory (LSTM) networks    -   e. Graph neural networks    -   f. Transformer networks    -   g. Random forest, boosted forest, XGBoost, etc.

FIG. 3 is a flowchart illustrating an exemplary method for training andusing a machine learning system for simultaneously detect and gradeprostate cancer, according to one or more exemplary embodiments of thepresent disclosure. Cancer grading may measure the differentiation ofcancer cells from normal tissue, and it may be assessed at both a locallevel by inspecting the cell morphology as well as slide-level summariescontaining the relative quantities of grades. Grading may be performedas part of a pathologist's diagnostic report for common cancers such asprostate, kidney and breast. The exemplary methods 300 and 320 may beused to train and use a machine learning system to simultaneously detectand grade prostate cancer.

According to one embodiment, the exemplary methods 300 and 320 mayinclude one or more of the following steps. In step 301, the method mayinclude receiving one or more digital images of a stained prostatetissue specimen into a digital storage device. The digital storagedevice may comprise a hard drive, a network drive, a cloud storage, arandom access memory (RAM), etc.

In step 303, the method may include receiving at least one label for theone or more digital images, wherein the at least one label contains anindication of a presence of cancer and a cancer grade. The cancer grademay comprise a primary and a secondary Gleason grade.

In step 305, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 307, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. Detectingtissue regions and removing non-tissue tiles may be accomplished bythresholding methods based on color, color intensity, texture features,Otsu's method, etc., followed by running a connected componentsalgorithm. The thresholding may provide labels on tissue vs. non-tissueregions for one or more pixels of each received slide image, based onthe thresholding method. The connected components algorithm may detectimage regions or pixels connected to one another, to detect tissueversus non-tissue regions across entire image regions, slide images, orslides. Detecting tissue regions and removing non-tissue tiles may alsobe accomplished by segmentation algorithms, such as k-means, graph cuts,Mask R-CNN, etc.

In step 309, the method may include training a machine learning model topredict if cancer is present and a grade of cancer for the one or moredigital images. Training may be accomplished in a variety of ways,including but not limited to:

-   -   a. Training a CNN to predict primary, secondary, and/or tertiary        grades using an MIMLL model, as disclosed above, for example,        via treating each slide as a set of tiles associated with        multiple labels, selecting slides that correspond to synoptic        training labels, scoring each tile by its relevance to a label,        and updating weights of the CNN model with respect to associated        label assignments. The trained CNN may be used to extract        embeddings from each tile in a set of slides, to train a        multi-task aggregator (e.g., the previously disclosed        aggregation model) to predict the presence of cancer, cancer        Gleason grade group, and/or the primary, secondary, and tertiary        grade of each tile or slide. Alternatively, the prediction        output from each tile may be used and aggregated with        hand-designed post-processing methods, e.g., having each tile        vote for each grade and taking the majority vote.    -   b. Using a MIL model, classify each tile as cancerous or benign,        and transfer the grading labels for the “pure” cases where        primary/secondary/tertiary grades are the same grade. Train a        tile-level classifier with the transferred labels using        supervised learning. Refine the model using self-supervised        learning as disclosed in the weakly supervised learning module        above.    -   c. Extract features/embeddings from each tile, and then use the        multi-task aggregator (e.g., the aggregation model disclosed        above) to predict the presence of cancer, cancer Gleason grade        group, and/or the primary, secondary, and tertiary grade.        Embeddings may be from a pre-trained CNN, random features,        features from an unsupervised clustering model, SIFT, ORB, etc.

In step 321, the method may include receiving one or more digital imagesof a stained prostate specimen into a digital storage device. Thedigital storage device may comprise a hard drive, a network drive, acloud storage, a RAM, etc.

In step 323, the method may include partitioning the one or more digitalimages into a collection of tiles.

In step 325, the method may include detecting at least one tissue regionfrom a background of a digital image to create a tissue mask andremoving at least one tile that is non-tissue. Detecting may be achievedin a variety of ways, including but not limited to: thresholdingmethods, based on color, color intensity, texture features, Otsu'smethod, or any other suitable method, followed by running a connectedcomponents algorithm; and segmentation algorithms such as k-means, graphcuts, Mask R-CNN, or any other suitable method.

In step 327, the method may include applying a trained machine learningmodel to the collection of tiles to predict the presence of cancer and agrade of cancer. The grade of cancer may comprise a cancer Gleason gradegroup, and/or a primary, a secondary, and a tertiary grade group.

In step 329, the method may include outputting a prediction, for exampleto an electronic storage device.

FIG. 4 is a flowchart illustrating an exemplary method for training andusing a machine learning system for tumor quantification in prostateneedle biopsies, according to one or more exemplary embodiments of thepresent disclosure. Tumor quantification for prostate needle biopsiesmay be comprised of estimating the total and relative volumes of cancerfor each cancer grade (e.g., a Gleason grade). Tumor quantification mayplay an important role in understanding the composition and severity ofprostate cancer, and it may be a common element on pathology diagnosticreports. Quantifying tumor size may be traditionally performed manuallywith a physical ruler on a glass slide. Manual quantification in thismanner may suffer from both inaccuracy and consistency. The exemplarymethods 400 and 420 may be used to train and use a machine learningsystem to quantify a tumor in prostate needle biopsies.

According to one embodiment, the exemplary methods 400 and 420 mayinclude one or more of the following steps. In step 401, the method mayinclude receiving one or more digital images of a stained prostatetissue specimen into a digital storage device. The digital storagedevice may comprise a hard drive, a network drive, a cloud storage, arandom access memory (RAM), etc.

In step 403, the method may include receiving at least one real-valuedtumor quantification label for each of the one or more digital images,wherein the at least one real-valued tumor quantification label containsan indication of a primary grade and a secondary grade. The label mayalso include a respective volume, a respective length, and a respectivesize of the tumor in the one or more digital images.

In step 405, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 407, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 409, the method may include training a machine learning model tooutput a cancer grading prediction, as described in exemplary method300. Tumor quantification estimates may be estimated in many ways,including but not limited to:

-   -   a. Counting the number of tiles of grade, and geometrically        estimating their volume and ratios relative to the volume of        benign tissue.    -   b. Train a model using a slide-level grading module, e.g., as        described in exemplary method 300. This model may take, as        input, a tile-level diagnostic features from a machine learning        cancer grading prediction model (e.g., the model trained in        exemplary method 300), and output each tumor quantification        metric using a real valued regression model.

In step 421, the method may include receiving one or more digital imagesof a stained prostate specimen into a digital storage device. Thedigital storage device may comprise a hard drive, a network drive, acloud storage, a random access memory (RAM), etc.

In step 423, the method may include partitioning the one or more digitalimages into a collection of tiles.

In step 425, the method may include detecting at least one tissue regionfrom a background of a digital image to create a tissue mask andremoving at least one tile that is non-tissue. This may be achieved in avariety of ways, including but not limited to: thresholding methods,based on color, color intensity, texture features, Otsu's method, or anyother suitable method, followed by running a connected componentsalgorithm; and segmentation algorithms such as k-means, graph cuts, MaskR-CNN, or any other suitable method.

In step 427, the method may include applying a trained machine learningmodel to the collection of tiles to compute a tumor quantificationprediction. The prediction may be output to an electronic storagedevice. Tumor quantification may be in the form of size metrics orpercentages.

In step 429, the method may include outputting a prediction to anelectronic storage device.

FIG. 5 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting a cancer subtype,according to one or more exemplary embodiments of the presentdisclosure. Many cancers have multiple subtypes. For example, in breastcancer, it may be determined whether a cancer is invasive or not, if itis lobular or ductal, and if various other attributes are present, suchas calcifications. This method of predicting a cancer subtype mayinclude a prediction of multiple, non-exclusive, categories that mayinvolve the use of multi-label learning.

According to one embodiment, the exemplary methods 500 and 520 mayinclude one or more of the following steps. In step 501, the method mayinclude receiving one or more digital images associated with a tissuespecimen into a digital storage device. The digital storage device maycomprise a hard drive, a network drive, a cloud storage, a random accessmemory (RAM), etc.

In step 503, the method may include receiving a plurality of labels forthe one or more digital images, wherein the plurality of labels and/or abiomarker of the tissue specimen. In a breast cancer specimen, arelevant biomarker could be a presence of calcifications, presence orabsence of cancer, ductal carcinoma in situ (DCIS), invasive ductalcarcinoma (IDC), inflammatory breast cancer (IBC), Paget disease of thebreast, angiosarcoma, phyllodes tumor, invasive lobular carcinoma,lobular carcinoma in situ, and various forms of atypia. Labels may notnecessarily mutually exclusive and multiple subtypes may besimultaneously observed.

In step 505, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 507, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 509, the method may include training a machine learning model topredict a form and/or subtype of cancer for each tile and/or slide.Training the machine learning model may be accomplished using the MIMLLmodel disclosed above. The trained subtype prediction machine learningmodel may be refined using a slide-level prediction model (e.g., anaggregation model) as disclosed above. The slide-level prediction modelmay take, as input, tile-level subtype predictions from an MIMLL model,and output slide-level predictions indicating the presence of eachcancer subtype.

In step 521, the method may include receiving one or more digital imagesassociated with a tissue specimen into a digital storage device. Thedigital storage device may comprise a hard drive, a network drive, acloud storage, a random access memory (RAM), etc.

In step 523, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 525, the method may include partitioning the one or more digitalimages into a collection of tiles and discarding any tiles that do notcontain tissue.

In step 527, the method may include computing a cancer subtypeprediction from the collection of tiles and output the prediction to anelectronic storage device.

FIG. 6 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting a surgical margin,according to one or more exemplary embodiments of the presentdisclosure. When a tumor is surgically removed from a patient, it may beimportant to assess if the tumor was completely removed by analyzing themargin of tissue surrounding the tumor. The width of this margin and theidentification of any cancerous tissue in the margin may play animportant role for determining how a patient may be treated. Training amodel to predict margin width and composition may take the form ofmulti-label multi-task learning.

According to one embodiment, the exemplary methods 600 and 620 mayinclude one or more of the following steps. In step 601, the method mayinclude receiving one or more digital images associated with a tissuespecimen into a digital storage device. The digital storage device maycomprise a hard drive, a network drive, a cloud storage, a random accessmemory (RAM), etc.

In step 603, the method may include receiving a plurality of labels forthe one or more digital images, wherein the plurality of labels indicatea tumor margin and whether a margin is positive (e.g., tumor cells arefound in the margin), negative (e.g., the margin is entirely free ofcancer) or close (e.g., not definitively positive or negative).

In step 605, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 607, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 609, the method may include training a machine learning model topredict a cancer detection, presence, or grade, as disclosed above.

In step 621, the method may include receiving one or more digital imagesassociated with a tissue specimen into a digital storage device. Thedigital storage device may comprise a hard drive, a network drive, acloud storage, a random access memory (RAM), etc.

In step 623, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 625, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 627, the method may include computing a surgical margin, tumormargin size, or tumor composition prediction from the tiles. The methodmay also include outputting the prediction to an electronic storagedevice.

FIG. 7 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting a bladder cancerbiomarker, according to one or more exemplary embodiments of the presentdisclosure. Bladder cancer is one of the most common cancers in theworld. If bladder cancer is detected, the pathologist may also determineif muscularis propria is present on any of the slides where bladdercancer is detected. Muscularis propria is a layer of smooth muscle cellsforming a significant portion of the bladder wall. Detecting thepresence or absence of the muscularis propria is an important steptowards determining if bladder cancer is invasive or not. The embodimentperforms both cancer detection and muscularis propria detection, butcould be extended to any number of binary classification tasks.

According to one embodiment, the exemplary methods 700 and 720 mayinclude one or more of the following steps. In step 701, receiving oneor digital images associated with a tissue specimen into a digitalstorage device. The digital storage device may comprise a hard drive, anetwork drive, a cloud storage, a random access memory (RAM), etc.

In step 703, the method may include receiving a plurality of labels forthe one or more digital images, wherein the plurality of labels indicatea presence or absence of cancer or the presence/absence of muscularispropria.

In step 705, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 707, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 709, the method may include training a machine learning model,e.g., by using a weakly supervised learning module (as disclosed above)to train a MIMLL model, and aggregating output scores indicating thepresence/absence of cancer or the presence/absence of muscularis propriaacross multiple tiles. Alternatively, an aggregation model could betrained to predict multiple labels of each image, tile, or slide, usingembeddings from each tile.

In step 721, the method may include receiving one or more digital imagesassociated with a tissue specimen into a digital storage device. Thedigital storage device may comprise a hard drive, a network drive, acloud storage, a random access memory (RAM), etc.

In step 723, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 725, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 727, the method may include computing a muscularis propriaprediction or invasive cancer prediction from the collection of tiles.The method may also include outputting the prediction to an electronicstorage device.

FIG. 8 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting a pan-cancer diagnosis,according to one or more exemplary embodiments of the presentdisclosure. While machine learning has been successfully used to creategood models for predicting cancer in common cancer types, predictionsfor rare cancers are a challenge because there may be less trainingdata. Another challenge is predicting where a cancer originated when itis metastatic, and sometimes the determination is not possible. Knowingthe tissue of origin may help guide treatment of the cancer. Theembodiment allows for pan-cancer prediction and cancer of originprediction using a single machine learning model. By training on manytissue types, the method may achieve an understanding of tissuemorphology such that it may effectively generalize rare cancer typeswhere very little data may be available.

According to one embodiment, the exemplary methods 800 and 820 mayinclude one or more of the following steps. In step 801, receiving oneor digital images associated with a tissue specimen into a digitalstorage device. The digital storage device may comprise a hard drive, anetwork drive, a cloud storage, a random access memory (RAM), etc.

In step 803, the method may include receiving a plurality of datadenoting a type of tissue shown in each of the digital images receivedfor a patient.

In step 805, the method may include receiving a set of binary labels foreach digital image indicating a presence or an absence of cancer.

In step 807, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 809, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 811, the method may include organizing at least one pan-cancerprediction output for a patient into a binary list. One element of thelist may indicate the presence of any cancer, and other elements in thelist may indicate the presence of each specific cancer type. Forexample, a prostate cancer specimen may have a positive indicator forgeneral cancer, a positive indicator for prostate indicator for prostatecancer, and negative indicators for all other outputs corresponding toother tissues (e.g., lung, breast, etc.). A patient for which all slidesare benign may have the label list contain all negative indicators.

In step 813, the method may include training a machine learning model topredict a binary vector for the patient. The machine learning model maycomprise a MIMLL model as described above, wherein a weakly supervisedlearning module may train a MIMLL model. Additionally, the method mayinclude aggregating pan-cancer prediction outputs of the MIMLL acrossvarious tiles, using an aggregation model (as disclosed above).Alternatively, an aggregation model may be trained to predict (multiple)pan-cancer prediction labels using embeddings from each tile.

In step 821, the method may include receiving one or more digital imagesassociated with a tissue specimen into a digital storage device. Thedigital storage device may comprise a hard drive, a network drive, acloud storage, a random access memory (RAM), etc.

In step 823, the method may include receiving a plurality of datadenoting a type of tissue shown in each of the digital images receivedfor a patient.

In step 825, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 827, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 829, the method may include computing a pan-cancer predictionusing a trained machine learning model. The machine learning model maycomprise the trained MIMLL model and/or aggregation model (as disclosedabove). Exemplary outputs may include, but are not limited to thefollowing:

-   -   a. Pan-cancer prediction: cancer presence output(s) may be used        to determine the present of cancer regardless of tissue type,        even for tissue types not observed during training. This may be        helpful for rare cancers where there may not be enough data        available to train a machine learning model.    -   b. Cancer of origin prediction: cancer sub-type output(s) may be        used to predict an origin of metastatic cancers by identifying        the largest sub-type output. If one of the cancer outputs for a        subtype is sufficiently higher than the type of tissue input to        the system, then this may indicate to a pathologist that output        is the cancer of origin. For example, if a bladder tissue        specimen is found to have cancer by the machine learning        model(s), but the prostate cancer sub-type output, this may        indicate to a pathologist that the cancer found in the bladder        may be metastasized prostate cancer instead of cancer that        originated in the bladder.

In step 831, the method may include saving the prediction to anelectronic storage device.

FIG. 9 is a flowchart illustrating an exemplary method for training andusing a machine learning system for predicting an organ toxicity,according to one or more exemplary embodiments of the presentdisclosure. In pre-clinical animal studies for drug development,pathologists determine if any toxicity is present, the form of toxicity,and/or the organs the toxicity may be found within. The embodimentenables performing these predictions automatically. A challenge withpre-clinical work is that a slide may contain multiple organs to saveglass during preparation.

According to one embodiment, the exemplary methods 900 and 920 mayinclude one or more of the following steps. In step 901, receiving oneor digital images associated with a tissue specimen into a digitalstorage device. The digital storage device may comprise a hard drive, anetwork drive, a cloud storage, a random access memory (RAM), etc.

In step 903, the method may include receiving a plurality of binarylabels indicating a present or an absence of toxicity and/or a type orseverity of toxicity.

In step 905, the method may include receiving a presence or an absenceof toxicity for at least one organ and/or its type or severity.

In step 907, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 909, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. This maybe achieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 911, the method may include organizing at least one toxicityprediction output for a patient into a binary list. One element of thelist may indicate the presence or type of any toxicity found on theslide, and other elements in the list may indicate the presence/type oftoxicity in each organ.

In step 913, the method may include training a machine learning model topredict a binary vector for the patient. The machine learning model maycomprise a MIMLL model as described above, wherein a weakly supervisedlearning module may train a MIMLL model. Additionally, the method mayinclude aggregating toxicity prediction outputs of the MIMLL acrossvarious tiles, using an aggregation model (as disclosed above).Alternatively, an aggregation model may be trained to predict toxicityprediction labels using embeddings from each tile.

In step 921, the method may include receiving one or more digital imagesassociated with a tissue specimen into a digital storage device. Thedigital storage device may comprise a hard drive, a network drive, acloud storage, a random access memory (RAM), etc.

In step 923, the method may include partitioning each of the one or moredigital images into a collection of tiles.

In step 925, the method may include detecting at least one tissue regionfrom a background of each of the one or more digital images to create atissue mask and removing at least one tile that is non-tissue. Furtherprocessing may commence without the non-tissue tiles. This may beachieved in a variety of ways, including but not limited to:thresholding methods, based on color, color intensity, texture features,Otsu's method, or any other suitable method, followed by running aconnected components algorithm; and segmentation algorithms such ask-means, graph cuts, Mask R-CNN, or any other suitable method.

In step 927, the method may include computing a toxicity predictionusing a trained machine learning model. The machine learning model maycomprise the trained MIMLL model and/or aggregation model (as disclosedabove). Exemplary outputs may include, but are not limited to thefollowing:

-   -   a. Toxicity presence: a toxicity presence output may be used to        determine the presence and/or severity of toxicity, regardless        of tissue type across the entire slide.    -   b. Organ toxicity prediction: an organ toxicity output may be        used to determine which organ the toxicity may be found within.

In step 929, the method may include saving the toxicity prediction to anelectronic storage device.

FIG. 10 illustrates an exemplary connected components algorithm,according to an embodiment of the disclosure. The connected componentsalgorithm may aggregate features across image regions. For example,thresholding may yield a binary (e.g., black and white) image. Aconnected components algorithm or model may identify various regions inthe image, e.g., 3 regions (green, red, brown) at the pixel level. Eachpixel may belong to a tile and a component (green, red, or brown) in thespecific implementation using connected components. Aggregation mayoccur in many ways, including majority vote (e.g., for all tiles in thegreen component vote, resulting in green having a value of 1) or alearned aggregator (e.g., in which a vector of features may be extractedfrom each tile and input to a components aggregator module run for eachcomponent, so tiles in the green component would be fed into acomponents aggregator module which may produce a grade number). A CNNmay output either a prediction (e.g., a number) for a tile, a featurevector for a tile that describes its visual properties, or both.

As shown in FIG. 11 , device 1100 may include a central processing unit(CPU) 1120. CPU 1120 may be any type of processor device including, forexample, any type of special purpose or a general-purpose microprocessordevice. As will be appreciated by persons skilled in the relevant art,CPU 1120 also may be a single processor in a multi-core/multiprocessorsystem, such system operating alone, or in a cluster of computingdevices operating in a cluster or server farm. CPU 1120 may be connectedto a data communication infrastructure 1110, for example a bus, messagequeue, network, or multi-core message-passing scheme.

Device 1100 may also include a main memory 1140, for example, randomaccess memory (RAM), and also may include a secondary memory 1130.Secondary memory 1130, e.g. a read-only memory (ROM), may be, forexample, a hard disk drive or a removable storage drive. Such aremovable storage drive may comprise, for example, a floppy disk drive,a magnetic tape drive, an optical disk drive, a flash memory, or thelike. The removable storage drive in this example reads from and/orwrites to a removable storage unit in a well-known manner. The removablestorage may comprise a floppy disk, magnetic tape, optical disk, etc.,which is read by and written to by the removable storage drive. As willbe appreciated by persons skilled in the relevant art, such a removablestorage unit generally includes a computer usable storage medium havingstored therein computer software and/or data.

In alternative implementations, secondary memory 1130 may includesimilar means for allowing computer programs or other instructions to beloaded into device 1100. Examples of such means may include a programcartridge and cartridge interface (such as that found in video gamedevices), a removable memory chip (such as an EPROM or PROM) andassociated socket, and other removable storage units and interfaces,which allow software and data to be transferred from a removable storageunit to device 1100.

Device 1100 also may include a communications interface (“COM”) 1160.Communications interface 1160 allows software and data to be transferredbetween device 1100 and external devices. Communications interface 1160may include a modem, a network interface (such as an Ethernet card), acommunications port, a PCMCIA slot and card, or the like. Software anddata transferred via communications interface 1160 may be in the form ofsignals, which may be electronic, electromagnetic, optical or othersignals capable of being received by communications interface 1160.These signals may be provided to communications interface 1160 via acommunications path of device 1100, which may be implemented using, forexample, wire or cable, fiber optics, a phone line, a cellular phonelink, an RF link or other communications channels.

The hardware elements, operating systems, and programming languages ofsuch equipment are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith. Device 1100 mayalso include input and output ports 1150 to connect with input andoutput devices such as keyboards, mice, touchscreens, monitors,displays, etc. Of course, the various server functions may beimplemented in a distributed fashion on a number of similar platforms,to distribute the processing load. Alternatively, the servers may beimplemented by appropriate programming of one computer hardwareplatform.

Throughout this disclosure, references to components or modulesgenerally refer to items that logically can be grouped together toperform a function or group of related functions. Like referencenumerals are generally intended to refer to the same or similarcomponents. Components and modules may be implemented in software,hardware or a combination of software and hardware.

The tools, modules, and functions described above may be performed byone or more processors. “Storage” type media may include any or all ofthe tangible memory of the computers, processors, or the like, orassociated modules thereof, such as various semiconductor memories, tapedrives, disk drives and the like, which may provide non-transitorystorage at any time for software programming.

Software may be communicated through the Internet, a cloud serviceprovider, or other telecommunication networks. For example,communications may enable loading software from one computer orprocessor into another. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

The foregoing general description is exemplary and explanatory only, andnot restrictive of the disclosure. Other embodiments of the inventionwill be apparent to those skilled in the art from consideration of thespecification and practice of the invention disclosed herein. It isintended that the specification and examples to be considered asexemplary only.

What is claimed is:
 1. A computer-implemented method for processingelectronic slide images, the method comprising: receiving one or moreelectronic slide images associated with a tissue specimen, the tissuespecimen being associated with a patient and/or medical case;determining a prediction, using a machine learning prediction model, forat least one label for the one or more electronic slide images, themachine learning prediction model having been generated by partitioningone of a plurality of training images into a plurality of training tilesfor the plurality of training images; creating a training tissue mask bydetecting at least one tissue region from a background of the one ormore electronic slide images; removing at least one of the plurality oftiles detected to be non-tissue; and using the machine learningprediction model under weak supervision to infer at least one tile-levelprediction using at least one label of a plurality of synopticannotations of the plurality of training images.
 2. Thecomputer-implemented method of claim 1, wherein the plurality of tilesthat are determined to be non-tissue are further determined to be abackground of the tissue specimen.
 3. The computer-implemented method ofclaim 1, further comprising: detecting a plurality of tissue regions ofthe one or more electronic slide images and/or plurality of tiles bysegmenting the tissue regions from the background.
 4. Thecomputer-implemented method of claim 3, wherein the segmenting comprisesusing thresholding based on color, color intensity, and/or texturefeatures.
 5. The computer-implemented method of claim 1, wherein theplurality of training images comprise a plurality of electronic slideimages and a plurality of target labels.
 6. The computer-implementedmethod of claim 1, wherein using the machine learning prediction modelunder weak supervision comprises using multiple-instance learning (MIL),Multiple Instance Multiple Label Learning (MIMLL), self-supervisedlearning, and unsupervised clustering.
 7. The computer-implementedmethod of claim 1, wherein using the machine learning prediction modelunder weak supervision comprises using at least one of Multiple InstanceMultiple Label Learning (MIMLL), self-supervised learning, andunsupervised clustering.
 8. The computer-implemented method of claim 1,further comprising: receiving a plurality of predictions of at least onefeature from a weakly-supervised tile-level learning module for theplurality of training tiles; applying the machine learning model totake, as an input, the plurality of predictions of the at least onefeature from the weakly-supervised tile-level learning module for theplurality of training tiles; and predicting a plurality of labels for aslide or a patient specimen, using the plurality of training tiles. 9.The computer-implemented method of claim 8, wherein at least one of theplurality of labels is binary, categorical, ordinal or real-valued. 10.The computer-implemented method of claim 8, wherein applying the machinelearning model to take, as the input, the plurality of predictions ofthe at least one feature from the weakly-supervised tile-level learningmodule for the plurality of training tiles comprises a plurality ofimage features.
 11. The computer-implemented method of claim 1, whereinthe machine learning prediction model predicts at least one label usingat least one unseen slide.
 12. A system for processing electronic slideimages corresponding to a tissue specimen, the system comprising: atleast one memory storing instructions; and at least one processorconfigured to execute the instructions to perform operations comprising:receiving one or more electronic slide images associated with the tissuespecimen; determining a prediction, using a machine learning predictionmodel, for at least one label for the one or more electronic slideimages, the machine learning prediction model having been generated bypartitioning one of a plurality of training images into a plurality oftraining tiles for the plurality of training images; creating a trainingtissue mask by detecting at least one tissue region from a background ofthe one or more electronic slide images; removing at least one of theplurality of tiles detected to be non-tissue; and using the machinelearning prediction model under weak supervision to infer at least onetile-level prediction using at least one label of a plurality ofsynoptic annotations of the plurality of training images.
 13. The systemof claim 12, wherein the plurality of training tiles that are determinedto be non-tissue are further determined to be a background of the tissuespecimen.
 14. The system of claim 12, further comprising: detecting aplurality of tissue regions of the one or more electronic slide imagesand/or plurality of tiles by segmenting the tissue regions from thebackground.
 15. The system of claim 14, wherein the segmenting comprisesusing thresholding based on color, color intensity, and/or texturefeatures.
 16. The system of claim 12, wherein the plurality of trainingelectronic slide images comprise a plurality of electronic slide imagesand a plurality of target labels.
 17. The system of claim 12, whereinusing the machine learning prediction model under weak supervisioncomprises using multiple-instance learning (MIL), Multiple InstanceMultiple Label Learning (MIMLL), self-supervised learning, andunsupervised clustering.
 18. The system of claim 12, further comprising:receiving a plurality of predictions of at least one feature from aweakly-supervised tile-level learning module for the plurality oftraining tiles; applying the machine learning model to take, as aninput, the plurality of predictions of the at least one feature from theweakly-supervised tile-level learning module for the plurality oftraining tiles; and predicting a plurality of labels for a slide or apatient specimen, using the plurality of training tiles.
 19. The systemof claim 18, wherein at least one of the plurality of labels is binary,categorical, ordinal or real-valued.
 20. A non-transitory computerreadable medium storing instructions that, when executed by a processor,cause the processor to perform a method for processing electronic slideimages corresponding to a tissue specimen, the method comprising:receiving one or more electronic slide images associated with a tissuespecimen, the tissue specimen being associated with a patient and/ormedical case; determining a prediction, using a machine learningprediction model, for at least one label for the one or more electronicslide images, the machine learning prediction model having beengenerated by partitioning one of a plurality of training images into aplurality of training tiles for the plurality of training images;creating a training tissue mask by detecting at least one tissue regionfrom a background of the one or more electronic slide images; removingat least one of the plurality of tiles detected to be non-tissue; andusing the machine learning prediction model under weak supervision toinfer at least one tile-level prediction using at least one label of aplurality of synoptic annotations of the plurality of training images.